Problem
Research questions and friction points this paper is trying to address.
Cooperative Perception
Sensing Occlusion
Collision Avoidance
Intelligent Transportation Systems
Local Dynamic Map
ITS Fairy: Occlusion Assistance Selected Against a Recipient's Own Perception Reports
🤖 AI Summary
This study addresses the failure of local safety applications caused by perceptual occlusion, which deprives vehicles of critical object states required for collision computation. To overcome this limitation, we propose an infrastructure-side cooperative service termed S-LDM. Departing from conventional predictive perception paradigms, S-LCM innovatively leverages receiver-reported data as a filtering criterion to precisely reconstruct missing conflict object states, thereby facilitating local obstacle avoidance. Application-level evaluations conducted using SUMO-ms-van3t and TCA demonstrate that the proposed approach significantly outperforms purely local solutions in occluded scenarios. Specifically, it effectively maintains multi-second safety time gaps, substantially enhancing overall driving safety under challenging visibility conditions.
📝 Abstract
Cooperative perception can expose object state beyond a vehicle's onboard sensors, but sensing occlusion can still leave a local safety application without the objects its collision computation needs. To tackle this challenge, we present the ITS Fairy, an infrastructure-side Server Local Dynamic Map (S-LDM) service whose decision unit is the pair (recipient, missing conflict-relevant object): among objects absent from a recipient's CPM-derived reported awareness, it sends only those relevant to a Time of Closest Approach (TCA) conflict test. Comparable services predict what a vehicle can perceive; the ITS Fairy instead reads what it has already reported. The recipient inserts the selected state into its local LDM and uses its unchanged collision-avoidance controller. We evaluate this application-level mechanism in SUMO--ms-van3t--S-LDM emulation, since extended as VaN3Twin, using a sensing-occluded lane merge and four-way intersection scenario. At every main-sweep speed, the smallest assisted per-encounter minimum TCA exceeds the largest local-only value in the archived data. Additionally, assisted medians remain in the multi-second range where local-only operation repeatedly approaches zero. In the lane-merge robustness data, the median benefit persists at 80% configured assistance omission with 10 and 5 Hz analysis, but largely disappears at 100-120 km/h when 80% omission is combined with 1 Hz analysis. These results demonstrate the application-level value of supplying object state selected against what a recipient has itself reported. They are not a vehicular wireless-channel evaluation, and they do not quantify what selectivity saves relative to forwarding every nearby object.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cooperative Perception
Server Local Dynamic Map
Occlusion Assistance
Time of Closest Approach
Collective Perception Message
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